Designing Protected Areas Networks in the North: Identifying Representative Area and the Use of Focal Species in a Yukon Case Study
Bibliographic record
Abstract
The science of conservation biology has made many contributions to improving biodiversity conservation within protected areas around the globe. Northern ecosystems are unique, and principles for protected areas design developed for temperate and tropical ecoregions may not readily be extrapolated to northern regions. Recent increases in ecological threats to the Canadian North have spurred interest in improving conservation and representation of northern ecosystems. Here, I present an overview of issues relevant to protected areas planning in the Canadian North, with a focus on the Yukon. I highlight recent Northern Research Institute- supported research on protected areas design in the Yukon, with a particular focus on the issue of representation and an examination of the potential utility of so-called species in identifying the location of representative protected areas. I show how Geographic Information Systems (GIS) may be applied to test questions of how many protected areas may be required to adequately represent mammal diversity in the ecoregions of the Yukon. I also use two different approaches to identify focal species for the Yukon to show that there is a great deal of ambiguity involved in how these species are identified.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".